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Comparisons of Parallel Preconditioners for the Computation of Interior Eigenvalues by the Minimization of Rayleigh Quotient

레이레이 계수의 최소화에 의한 내부고유치 계산을 위한 병렬준비행렬들의 비교

  • 마상백 (한양대학교 전자컴퓨터 공학부) ;
  • 장호종 (한양대학교 수학과)
  • Published : 2003.06.01

Abstract

Recently, CG (Conjugate Gradient) scheme for the optimization of the Rayleigh quotient has been proven a very attractive and promising technique for interior eigenvalues for the following eigenvalue problem, Ax=λx (1) The given matrix A is assummed to be large and sparse, and symmetric. Also, the method is very amenable to parallel computations. A proper choice of the preconditioner significantly improves the convergence of the CG scheme. We compare the parallel preconditioners for the computation of the interior eigenvalues of a symmetric matrix by CG-type method. The considered preconditioners are Point-SSOR, ILU (0) in the multi-coloring order, and Multi-Color Block SSOR (Symmetric Succesive OverRelaxation). We conducted our experiments on the CRAY­T3E with 128 nodes. The MPI (Message Passing Interface) library was adopted for the interprocessor communications. The test matrices are up to $512{\times}512$ in dimensions and were created from the discretizations of the elliptic PDE. All things considered the MC-BSSOR seems to be most robust preconditioner.

최근에 CG 반복법을 이용하여 레이레이 계수를 최소화함으로써 대칭행렬의 내부고유치를 구하는 방법이 개발되었다 그리고 이 방법은 병렬계산에 매우 적합하다. 적절한 준비행렬의 선택은 수렴속도를 향상시킨다. 우리는 본 연구에서 이를 위한 병렬준비행렬들을 비교한다. 고려된 준비행렬들은 Point-SSOR, 다중색채하의 ILU(0)와 Block SSOR이다. 우리는 128개의 노드를 가진 CRAY-T3E에서 구현하였다. 프로세서간의 통신은 MPI 리이브러리를 사용하였다. 최고 512$\times$512 행렬까지 시험하였는데 이 행렬들은 타원형 편미분방정식의 근사화에서 얻어졌다. 그 결과 다중색채 Block SSOR이 가장 성능이 우수한 것으로 판명되었다.

Keywords

References

  1. W.W. Bradbury and R. Fletcher, New iterative methods for the solution of the eigenproblem, Numer. Math., 9, pp. 259-267, 1966 https://doi.org/10.1007/BF02162089
  2. Y. Cho and Y.K. Yong, A multi-mesh, preconditioned conjugate gradient solver for eigenvalue problems in finite element models, Comput. Struct., 58, pp.575-583, 1996 https://doi.org/10.1016/0045-7949(95)00157-C
  3. H. Elman, Iterative methods for large, sparse, nonsymmetric systems of linear equations, Ph. D Thesis, Yale University, 1982
  4. Y.T. Feng and D.R.J. Owen, Conjugate gradient methods for solving the smallest eigenpair of large symmetric eigenvalue problems, Internat, J. Numer. Methods Engrg., 39, pp.2209-2229, 1996 https://doi.org/10.1002/(SICI)1097-0207(19960715)39:13<2209::AID-NME951>3.0.CO;2-R
  5. G. Gambolati, G. Pini and M. Putti, Nested iterations for symmetric eigenproblems, SIAM J. Sci. Comput., 16, pp.173-191, 1995 https://doi.org/10.1137/0916012
  6. G. Gambolati, F. Sartoretto and P. Florian, An orthogonal accelerated deflation technique for large symmetric eigenproblems, Comput. Methods Appl. Mech. Engrg., 94, pp.13-23, 1992 https://doi.org/10.1016/0045-7825(92)90154-C
  7. D.E. Longsine and S.F. McCormick, Simultaneous Rayleigh quotient minimization methods for $Ax={\lambda}Bx$, Linear Algebra Appl., 34, pp.195-234, 1980 https://doi.org/10.1016/0024-3795(80)90166-4
  8. Sangback Ma, Comparisons of the parallel preconditioners on the CRAY-T3E for large nonsymmetric linear systems, International Journal of High Speed Computing, 10, pp. 285-300, 1999 https://doi.org/10.1142/S0129053399000144
  9. B.N. Parlett, The Symmetric Eigenvalue Problem, Prentice-Hall, Englewood Cliffs, NJ, 1980
  10. A. Ruhe, Computation of eigenvalues and eigenvectors, in Sparse Matrix Techniques, V.A. Baker, ed., Springer-Verlag, Berlin, pp.130-184, 1977
  11. Y. Saad, Highly parallel preconditioner for general sparse matrices, in Recent Advances in Iterative Methods, IMA Volumes in Mathematics and its Applications, G. Golub, M. Luskin and A. Greenbaum, eds, Springer-Verlag, Berlin, Vol.60, pp.165-199, 1994
  12. F. Sartoretto, G. Pini and G. Gambolati, Accelerated simultaneous iterations for large finite element eigenproblems, J. Comput. Phys., 81, pp.53-69, 1989 https://doi.org/10.1016/0021-9991(89)90064-8
  13. H.R. Schwarz, Eigenvalue problems and preconditioning, ISNM, 96, pp.191-208, 1991